Creating modular conversations using implicit routing

ABSTRACT

A computer implemented method of routing a verbal input to one of a plurality of handlers, comprising using one or more processors adapted to execute a code, the code is adapted for receiving a verbal input from a user, applying a plurality of verbal content identifiers to the verbal input, each of the verbal content identifiers is adapted to evaluate an association of the verbal input with a respective one of a plurality of handlers by computing a match confidence value for one or more features, such as an intent expressed by the user and/or an entity indicated by the user, extracted from the verbal input and routing the verbal input to a selected one of the handlers based on the matching confidence value computed by the plurality of verbal content identifiers. The selected handler is adapted to initiate one or more actions in response to the verbal input.

BACKGROUND

The present invention, in some embodiments thereof, relates toautomatically routing verbal inputs received from users to verbalcontent handlers, and, more specifically, but not exclusively, toautomatically routing verbal inputs to the verbal content handlers usingimplicit routing based on content and optionally context of the verbalinputs.

Recent times have witnessed rapid and major advancements in development,deployment and use of automated systems, platforms and/or services.Human-machine interaction (HMI) may be a key element in many suchautomated environment applications, for example, mobile applications(e.g. mobile devices), lifestyle applications (e.g. artificial and/orvirtual personal assistants), commerce applications, transportation(e.g. autonomous vehicles) applications and/or the like.

Major advancements in computer technology, in hardware (e.g. increasedcomputing resources) as well as in computer science and software (e.g.algorithmic processing, machine learning, etc.) have paved the way fortransition from traditional HMI to more natural HMI. The traditional HMIimplementation may require the user to use intermediate devices, toolsand/or interfaces, for example, a keyboard, a pointing device (e.g.mouse, touchpad, etc.), a touchscreen and/or the like. The natural HMI,for example, voice, speech, gestures and/or the like on the other handmay relief the user of the need to use the intermediators and directlycommunicate with the machine, i.e. the computer, the mobile device, theautonomous vehicle and/or the like.

Among, the natural HMIs, speech and conversation may be of significantappeal as spoken language and conversational skills are acquired by mostpeople at early age and may be the most common and efficient way ofinteraction among people. Therefore employing the conversationalinteraction for the HMI implementation may be highly desirable. Suchspeech and conversation HMI implementation may apply one or more of aplurality of tools, techniques and/or methods, for example, speechrecognition, speech to text (STT) conversion, speech synthesis, naturallanguage processing (NLP), conversation construction and/or the like.

SUMMARY

According to a first aspect of the present invention there is a computerimplemented method of routing a verbal input to one of a plurality ofhandlers, comprising using one or more processors adapted to execute acode, the code is adapted for:

-   -   Receiving a verbal input from a user.    -   Applying a plurality of verbal content identifiers to the verbal        input, each of the plurality of verbal content identifiers is        adapted to evaluate an association of the verbal input with a        respective one of a plurality of handlers by computing a match        confidence value for one or more features extracted from the        verbal input. The one or more features comprise one or more of:        an intent expressed by the user and an entity indicated by the        user.    -   Routing the verbal input to a selected one of the plurality of        handlers based on the matching confidence value computed by the        plurality of verbal content identifiers, the selected handler is        adapted to initiate one or more actions in response to the        verbal input.

Using the plurality of (verbal content) handlers and efficiently routingthe verbal input(s) to these handlers may significantly reducecomplexity of analyzing the verbal input(s) and hence the dialog flowwith the user compared to analyzing the dialog flow in a singleworkspace. This reduced complexity may in turn increase accuracy of theestimation of the intent of the user, reduce and/or eliminate ambiguityembedded in the verbal input, reduce computation resources for analyzingthe verbal input(s), reduce latency in responding to the received verbalinput and/or the like. Moreover, implicitly routing the verbal input(s)to the preferred handler without requiring the user to explicitly statethe intended handler may relieve the user from keeping track andremembering which handler is designated for which intent and/or action.

According to a second aspect of the present invention there is a systemfor routing a verbal input to one of a plurality of handlers, comprisingone or more processors adapted to execute code, the code comprising:

-   -   Code instructions to receive a verbal input from a user.    -   Code instructions to apply a plurality of verbal content        identifiers to the verbal input, each of the plurality of verbal        content identifiers is adapted to evaluate an association of the        verbal input with a respective one of a plurality of handlers by        computing a match confidence value for one or more features        extracted from the verbal input. The one or more features        comprise one or more of: an intent expressed by the user and an        entity indicated by the user.    -   Code instructions to route the verbal input to a selected one of        the plurality of handlers based on the matching confidence value        computed by the plurality of verbal content identifiers, the        selected handler is adapted to initiate one or more actions in        response to the verbal input.

According to a third aspect of the present invention there is a computerprogram product for routing a verbal input to one of a plurality ofhandlers, comprising:

-   -   A non-transitory computer readable storage medium;    -   First code instructions for receiving a verbal input from a        user.    -   Second code instructions for applying a plurality of verbal        content identifiers to the verbal input, each of the plurality        of verbal content identifiers is adapted to evaluate an        association of the verbal input with a respective one of a        plurality of handlers by computing a match confidence value for        one or more features extracted from the verbal input. The one or        more features comprise one or more of: an intent expressed by        the user and an entity indicated by the user.    -   Third code instructions for routing the verbal input to a        selected one of the plurality of handlers based on the matching        confidence value computed by the plurality of verbal content        identifiers, the selected handler is adapted to initiate one or        more actions in response to the verbal input.        Wherein the first, second and third program instructions are        executed by one or more processors from the non-transitory        computer readable storage medium.

In a further implementation form of the first, second and/or thirdaspects, the verbal input comprises one or more members of a groupconsisting of: textual verbal input and speech verbal input. This mayallow applying the implicit routing to a plurality of applicationsdirected to interact with the user through textual and/or speechinteraction forms.

In an optional implementation form of the first, second and/or thirdaspects, the verbal input is segmented to a plurality of segments androuting each of the plurality of segments to one of the plurality ofhandlers according to the match confidence value computed by theplurality of verbal content identifiers for the each segment. Since adialog session with the user may include complex verbal inputs possiblyembedding multiple intended actions which may be managed by multiplehandlers, segmenting the verbal input may significantly simplifyanalysis, evaluation of association to the handlers and hence therouting to the selected handler.

In a further implementation form of the first, second and/or thirdaspects, each of the plurality of verbal content identifiers isassociated with a respective one of the plurality of handlers byadapting the each verbal content identifier to evaluate the associationaccording to one or more predefined features defined for the respectivehandler. As each verbal content identifier is associated with arespective handler, each verbal content identifier may be specificallyadapted, configured and/or trained according to the characteristicsand/or features of its respective handler thus improving the accuracy ofits evaluation association with the respective handler.

In a further implementation form of the first, second and/or thirdaspects, the match confidence value indicates a probability of the oneor more features to match a respective predefined feature. The matchconfidence value computed by each verbal content identifier may thusserve as a metrics for measuring the association level of the extractedfeature(s) with the respective handler.

In a further implementation form of the first, second and/or thirdaspects, the intent is extracted from the verbal input using one or moreverbal analysis tools, the intent is a member of a group consisting of:an intention, a purpose, an objective and a goal. The intent(s) may be akey element in understanding and/or classifying the action(s) intendedby the user and expressed in the verbal input. Therefore accuratelyextracting the intent(s) and associating it with one or more of thehandlers may be essential.

In a further implementation form of the first, second and/or thirdaspects, the entity is extracted from the verbal input using one or moreverbal analysis tools. The entity(s), for example, an object, an item,an element, a target device, a target application and/or the like may bea key element in understanding and/or classifying the action(s) intendedby the user and expressed in the verbal input. Therefore accuratelyextracting the entity(s) and associating it with one or more of thehandlers may be essential.

In an optional implementation form of the first, second and/or thirdaspects, the selected handler is selected based on one or more contextattributes provided by one or more of the plurality of verbal contentidentifiers. The one or more context attributes comprise one or more of:

-   -   An emotion of the user extracted using one or more voice        analysis tools,    -   A sentiment of the user extracted using one or more voice        analysis tools,    -   A geographical location of the user obtained from one or more        location detection tools, and    -   One or more previous features extracted from one or more        previous verbal inputs.        Using the context attributes may significantly increase the        accuracy of the selection of the selected handler since the        context of the verbal input, the context of the user and/or the        like may be highly indicative of the actual intent(s) of the        user.

In an optional implementation form of the first, second and/or thirdaspects, the selected handler is selected based on detection of one ormore mandatory entities extracted by one or more of the plurality ofverbal content identifiers, the one or more mandatory entities arepredefined to appear in the verbal input in conjunction with the intent.Basing the selection on mandatory predefined entity(s) may significantlyincrease the accuracy of the selection of the selected handler since incase of absence of entity(s) in the verbal input, one or more handlerswhich are predefined to mandatorily include such entity(s) may be ruledout as unappropriated for managing the verbal input.

In an optional implementation form of the first, second and/or thirdaspects, the selected handler is selected based on one or moreoperational attributes provided by one or more of the plurality ofverbal content identifiers, The one or more operational attributescomprise one or more of:

-   -   A threshold value,    -   A capability of the respective handler to manage the verbal        input,    -   A description of an analysis applied by one or more of the        plurality of verbal content identifiers to extract one or more        of the features,    -   A routing information relating to one or more previous verbal        inputs, and    -   Information obtained from at least another one of the plurality        of handlers.        Using the operational attributes may significantly increase the        accuracy of the selection of the selected handler since the        operational context of the handlers may be highly indicative of        the intent(s) of the user, in particular with respect to        previous verbal input(s) and/or dialog flows of the dialog        session with the user.

In an optional implementation form of the first, second and/or thirdaspects, the selected handler is selected according to a priorityassigned to at least some of the plurality of verbal contentidentifiers. Prioritizing the verbal content identifiers in particularaccording to their associated handlers may allow elevating and/ordecrease importance and/or criticality of the respective handlers overother handlers such that a certain handler may take precedence overanother handler even when estimated to be less associated with theverbal input.

In an optional implementation form of the first, second and/or thirdaspects, one or more of the plurality of handlers are filtered out incase their associated verbal content identifiers present a confidencevalue which fails to exceed a predefined threshold. This may allowsetting a minimal association level between a certain handler and theverbal input such that the handler may not be selected, i.e. filteredout in case its associated verbal content identifier computed a matchconfidence value lower than the predefined threshold.

In an optional implementation form of the first, second and/or thirdaspects, one or more of the plurality of handlers are filtered out incase they are indicated by one or more of their associated verbalcontent identifiers as incapable of managing the verbal input. This mayallow avoiding selecting handlers which are currently unavailable and/orincapable of managing the verbal input thus reducing complexity and/orlatency of the routing process.

In an optional implementation form of the first, second and/or thirdaspects, the verbal input is routed to a recent handler of the pluralityof handlers in case a similar the confidence value is computed bymultiple verbal content identifiers of the plurality of verbal contentidentifiers, the recent handler is a most recent handler to which aprevious verbal input was routed among a group of handlers associatedwith the multiple verbal content identifiers. Previous (historical)dialog flows may significantly imply on the current routing of theverbal input and may therefore significantly improve accuracy inselecting the selected handler.

In an optional implementation form of the first, second and/or thirdaspects, the verbal input is routed to a default handler of theplurality of handlers in case the confidence value computed by theplurality of verbal content identifiers fails to exceed a predefinedthreshold. The In case no clear resolution can be made in selecting theselected handler, the verbal input may be routed to the default handlerwhich may allow the user to better state, achieve and/or accomplish hisintent(s).

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of embodiments of the method and/or systemof the invention, several selected tasks could be implemented byhardware, by software or by firmware or by a combination thereof usingan operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced

In the drawings:

FIG. 1 is a flowchart of an exemplary process of automatically routing averbal input to verbal content handlers using implicit routing based oncontent and optionally context of the verbal inputs, according to someembodiments of the present invention; and

FIG. 2 is a schematic illustration of an exemplary system forautomatically routing a verbal input to verbal content handlers usingimplicit routing based on content and optionally context of the verbalinputs, according to some embodiments of the present invention.

DETAILED DESCRIPTION

The present invention, in some embodiments thereof, relates toautomatically routing verbal inputs received from users to verbalcontent handlers, and, more specifically, but not exclusively, toautomatically routing verbal inputs to the verbal content handlers usingimplicit routing based on content and optionally context of the verbalinputs.

According to some embodiments of the present invention, there areprovided methods, systems and computer program products for routing oneor more verbal inputs, for example, a textual input, a speech inputand/or the like received from one or more users to one or more verbalcontent handlers each adapted to manage (handle) one or more predefinedverbal content features expressed by the user(s).

As automated services (e.g. artificial and/or virtual personalassistants, autonomous vehicles, mobile devices, bots, internet bots,etc.) constantly evolve, interaction between the automated systems andapplications with human users and constructing dialog flows,specifically speech conversations becomes an essential key element.However, developing and/or supporting the interactive dialog flow forsuch systems and/or applications, in particular speech conversations maypresent a major challenge. It may require splitting, dividing and/orbreaking possible verbal inputs, either textual inputs and/or speechinputs into a set of features, for example, content, an intent(intention, purpose, objective, goal, etc.) of the user, an entity(object, item, element, target device, target application, etc.)indicated by the user, a dialog flow of the current dialog sessionand/or the like.

Moreover, as the interactive dialog applications continuously enhance,providing additional functionality, the interactive dialog flow maybecome significantly more elaborate presenting further challenges inmanaging and handling the dialog in order to respond accurately to theverbal inputs captured from the user. In particular, processing theverbal input in a single workspace may prove inefficient, inadequate andpossible impractical due to the complexity of the dialog. In order toovercome this limitation, a plurality of verbal content handlers may bedesigned, adapted and deployed to manage accurately different verbalinputs received from the user. Each of the verbal content handlers maybe a processing module, for example, a software agent, an applicationand/or the like adapted to initiate one or more actions in response tocertain predefined verbal inputs and/or part thereof received from theuser. The actions initiated by the verbal content handlers may includeone or more of a plurality of functions, features and/or operationsindicated by the user and supported by the automated service, systemand/or application. As each verbal input may be routed to its designatedverbal content handler, managing the interactive dialog, in particularthe speech conversation may be significantly improved and made moreefficient.

According to some embodiments of the present invention, routing theverbal inputs to their designated verbal content handler is doneimplicitly by estimating and/or predicting the intent (intention, goaland/or objective) of the user as expressed in the received verbalinput(s). This may be done by analyzing one or more verbal inputs toextract one or more features from the verbal input(s) received from auser and evaluate the intent of the user and routing the verbal input(s)to preferred verbal content handler(s) estimated to best serve theintent of the user. A correspondence (match) of the extracted featuresis evaluated with respect to feature(s) predefined for each of theverbal content handlers, thus an association of the verbal input to eachof the verbal content handler(s) is evaluated. This may significantlyreduce and possible eliminate ambiguity(s) in determining feature(s)embodied in the verbal input(s) and efficiently select automatically anappropriate (preferred) verbal content handler(s) which is estimated tobest serve the estimated intent.

The analysis of the verbal input(s) and extraction of the feature(s) maybe done by a plurality of verbal content identifiers each associatedwith a respective one of the verbal content handlers. Each of the verbalcontent identifiers may be specifically adapted, i.e. designed,configured, trained and/or the like to identify, in the verbal input(s),one or more predefined features associated with the respective verbalcontent handler. Each of the verbal content identifiers may apply one ormore verbal analysis tools, for example, a textual analysis, speechrecognition, a Natural Language Processing (NLP), a voice analysisand/or the like to extract the features. For each extracted feature, theverbal content identifiers may compute a match confidence value whichindicates of a probability that the extracted feature matches arespective predefined feature associated with the respective verbalcontent handler.

The extracted features coupled with their computed match confidencevalue received from the plurality of verbal content identifiers may beevaluated to select a preferred verbal content handler that is estimatedto best manage (handle, serve, etc.) the received verbal input, i.e. theverbal input is estimated to best associate with the selected verbalcontent handler. The verbal input is then routed to the selected verbalcontent handler which may take one or more actions in response to thereceived verbal input. The actions initiated by the verbal contenthandlers may include one or more of a plurality of functions, featuresand/or operations supported by the automated service, system and/orapplication interacting with the user.

Optionally, the verbal input is segmented to a plurality of segmentseach analyzed separately and provided to the verbal content identifierswhich may extract the feature(s) and compute their match confidencevalue. A preferred verbal content handler may then be selected from theplurality of verbal content handlers for each of the segments.

Optionally, the routing is based on one or more context attributesand/or operational attributes provided by one or more of the verbalcontent identifiers and/or globally stored or maintained. One or more ofthe verbal content identifiers may extract one or more contextattributes relating to the verbal input (e.g. feature(s) extracted fromprevious verbal inputs), the user (e.g. emotion, sentiment, geographicallocation, etc.) and/or the like. One or more of the verbal contentidentifiers may also report of one or more operational attributesrelating to, for example, their respective verbal content handler,previous routing events of their respective verbal content handler,operational attributes of one or more other verbal content handlersand/or the like. One or more of the context attributes and/or theoperational attributes may be used for selecting the preferred verbalcontent handler.

Implicitly routing the verbal input(s) to the preferred verbal contenthandler may present significant advantages compared to currentlyexisting methods for managing interactive dialog flows.

First, using the plurality of verbal content handlers and efficientlyrouting the verbal input(s) to these verbal content handlers maysignificantly reduce complexity of analyzing the verbal input(s) andhence the dialog flow compared to analyzing the dialog flow in a singleworkspace as may be done by some of the existing methods, for example,Watson Conversation Service (WCS), API.AI, wit.ai. and/or the like. Thereduced complexity may translate to several improvements in managing theverbal inputs and/or the dialog flow, for example, a more accurateestimation of the intent of the user (reduce and/or eliminate featuresambiguity features ambiguity in the verbal input), reduced computationresources for analyzing the verbal input(s), reduced latency inresponding to the received verbal input and/or the like.

Moreover, implicitly routing the verbal input(s) to the preferred verbalcontent handler may relieve the user from explicitly stating the verbalcontent handler that should be used for managing the current verbalinput as may be done by some of the existing methods, for example, Alexapersonal assistant by amazon and/or the like. As complexity of theautomated interactive dialogs increases the number of verbal contenthandlers may be extremely large forcing the user to keep track andremember which verbal content handler is designated for which intentand/or action in order to properly address (state) the selected verbalcontent handler. The Alexa virtual personal assistant in which theverbal content handlers are referred to as skills, the number of skillsrapidly increases and the user may need to uniquely associate and stateeach of his intentions and/or actions with a respective one of theskills. Therefore relieving the user from explicitly stating the verbalcontent handler to be used may present a major advantage.

Furthermore, implicitly routing the verbal input(s) to the preferredverbal content handler using the context attributes and/or theoperational attributes may significantly reduce and/or eliminatefeatures ambiguity in the verbal input thus increasing the accuracy inselecting the preferred verbal content handler. This is due to the factthat the contextual aspects relating to the verbal input(s) as well ascontextual aspects relating to the user may be highly indicative of theintent of the user and may therefore be used to improve the estimationof the intent expressed by the user in the received verbal input(s). Inaddition, the operational attributes of the verbal content handlers maybe highly useful to identify previous routing and/or management eventsof previous verbal input(s), specifically during the current dialogsession thus allowing more accurate selection of the preferred verbalcontent handler to manage the current verbal input.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages.

The computer readable program instructions may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider). In some embodiments, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may execute the computerreadable program instructions by utilizing state information of thecomputer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Referring now to the drawings, FIG. 1 is a flowchart of an exemplaryprocess of automatically routing a verbal input to verbal contenthandlers using implicit routing based on content and optionally contextof the verbal inputs, according to some embodiments of the presentinvention. A process 100 may be executed to route one or more verbalinputs, for example, a textual input, a speech input and/or the likereceived from one or more users to one or more verbal content handlerseach adapted to manage (handle) one or more predefined verbal contentfeatures expressed by the user(s). The routing is based on analyzing theverbal input and extracting one or more features, for example, content,intent (intention, purpose, objective, goal, etc.), entity (object,item, element, target device, target application, etc.), dialog flowsand/or the like identified in the verbal input. The analysis andextraction may be done by a plurality of verbal content identifiers eachassociated with a respective one of the verbal content handlers andadapted to identify in the verbal input one or more of the predefinedfeatures associated with the respective verbal content handler. Each ofthe verbal content identifiers may apply one or more verbal analysistools, for example, a textual analysis, speech recognition, a NaturalLanguage Processing (NLP), a voice analysis and/or the like to extractthe features. For each extracted feature, the verbal content identifiersmay compute a match confidence value indicative of a probability thatthe extracted feature matches a respective predefined feature associatedwith the respective verbal content handler.

The extracted features coupled with their computed match confidencevalue received from the plurality of verbal content identifiers areevaluated to select a preferred verbal content handler that is estimatedto best manage the received verbal input, i.e. the verbal input isestimated to best associate with the selected verbal content handler.The verbal input is then routed to the selected verbal content handlerwhich may take one or more actions in response to the verbal input.

Optionally, the verbal input is segmented to a plurality of segmentseach analyzed separately to extract the features and compute their matchconfidence value. A preferred verbal content handler may then beselected from the plurality of verbal content handlers for each of thesegments.

Optionally, the routing is based on one or more context attributesand/or operational attributes provided by one or more of the verbalcontent identifiers. One or more of the verbal content identifiers mayextract one or more context attributes relating to the verbal input(e.g. feature(s) extracted from previous verbal inputs), the user (e.g.emotion, sentiment, geographical location, etc.) and/or the like. One ormore of the verbal content identifiers may also report of one or moreoperational attributes relating to, for example, their respective verbalcontent handler, previous routing events, one or more other verbalcontent handlers and/or the like. One or more of the context attributesand/or the operational attributes may be used for selecting thepreferred verbal content handler.

Reference is also made to FIG. 2, which is a schematic illustration ofan exemplary system for automatically routing a verbal input to verbalcontent handlers using implicit routing based on content and optionallycontext of the verbal inputs, according to some embodiments of thepresent invention. An exemplary system 200 for classifying verbal inputsfrom one or more users 250 includes a computing device 201, for example,a processing node, a computer, a laptop, a server, a mobile device (e.g.a tablet, a cellular device, a Smartphone, etc.), a home applianceintegrating a processing device, a processing device of an autonomousvehicle and/or any processing device having one or more processor. Thecomputing device 201 comprises an Input/Output (I/O) interface 202, aprocessor(s) 204 and storage 206.

The I/O interface 202 may include one or more interfaces for interactingwith the user(s) 250, in particular interface(s) for capturing verbalinputs from the user(s) 250. The verbal input may include, for example,a textual input, a speech input and/or the like. Accordingly the I/Ointerface 202 may include one or more audio interfaces, for example, amicrophone to capture speech spoken (uttered) by the user(s) 250. TheI/O interface 202 may also include one or more textual interfaces, forexample, a keyboard, a touchscreen, a digital pen and/or the like tocapture text inserted by the user(s) 250. The I/O interface 202 mayfurther include one or more wired and/or wireless network interfaces forconnecting to one or more networks, for example, a Local area Network(LAN), a Wide area Network (WAN), a Metropolitan Area Network (MAN), acellular network, and/or the internet to facilitate communication withmore or more remote locations and/or resources.

The processor(s) 204, homogenous or heterogeneous, may include one ormore processors arranged for parallel processing, as clusters and/or asone or more multi core processor(s). The storage 206 may include one ormore non-transitory persistent storage devices, for example, a harddrive, a Flash array and/or the like. The storage 206 may furthercomprise one or more network storage devices, for example, a storageserver, a network accessible storage (NAS), a network drive, and/or thelike. The storage 206 may also include one or more volatile devices, forexample, a Random Access Memory (RAM) component and/or the like.

The storage 206 may store one or more software modules, for example, anOS, an application, a tool, an agent, a service, a script and/or thelike each comprising a plurality of program instructions that may beexecuted by the processor(s) 204 from the storage 206. For example, theprocessor(s) 204 may execute a verbal content router 210 software modulefor routing verbal inputs captured from the user(s) 250 to one or moreverbal content handlers 212 software modules adapted to initiate one ormore actions in response to the verbal inputs. The actions initiated bythe verbal content handlers 212 may include one or more of a pluralityof functions, features and/or operations supported by the computingdevice 201. Each of the verbal content handlers 212 may be adapted tomanage (handle) one or more verbal inputs and initiate one or moreactions in response to detection of one or more predefined verbal inputsassociated with the respective verbal content handler 212. Thepredefined verbal inputs may be identified and/or characterized by oneor more features which may be extracted from the received verbal input,for example, content, intent (intention, purpose, objective, goal,etc.), entity (object, item, element, target device, target application,etc.), dialog flows and/or the like.

Optionally, the verbal content router 210 and/or one or more of theverbal content handlers 212 are executed by one or more remoteplatforms, for example, a remote server, a cloud computing platform,such as, for example, Amazon Web Service (AWS), Google Cloud, MicrosoftAzure and/or the like. Additionally, and/or alternatively, the verbalcontent router 210 and/or one or more of the verbal content handlers 212may be implemented as one or more remote services, a remote service, acloud service, Software as a Service (SaaS), a Platform as a Service(PaaS) and/or the like. In such implementations, the computing device201 may execute a local agent that may communicate over the network withthe remotely executed verbal content router 210 and/or the verbalcontent handler(s) 222. The local agent may thus relay the capturedverbal speech input(s) (e.g. the textual input and/or the speech input)the remotely executed verbal content router 210 and/or the speechprocessing module(s) 222 for processing.

Optionally, the verbal content router 210 and one or more of the verbalcontent handlers 212 are executed by an automated software agentplatform, system and/or application, for example, a bot, an internet botand/or the like which communicates over the network with one or morecomputing devices such as the computing device 201 used by the users 250to receive the verbal input captured from the user(s) 250.

As shown at 102, the process 100 starts with the verbal content router210 receiving a verbal input captured from the user 250 through the I/Ointerface 202. The verbal input may include textual content, speechcontent and/or the like and is captured accordingly through interfacesof the I/O interface 202 suited for each type of the verbal input. Whileverbal inputs in the form of the textual content are supported by thesystem 200 through execution of the process 100, verbal inputs in theform of the speech content may be of particular significance.

Optionally, the verbal content router 210 segments that verbal input toa plurality of segments each processed independently through the process100.

As shown at 104, the verbal content router 210 may apply a plurality ofverbal content identifiers to the received verbal input, for example,the content router 210 may provide the verbal input to the plurality ofverbal content identifiers. Each of the verbal content identifiers isassociated with a respective one of the verbal content handlers 212. Assuch each verbal content handler 212 may register its set (one or more)of associated verbal content identifiers at the verbal content router210 to indicate the verbal content router 210 to apply the registeredverbal content identifiers to the verbal input.

Since each of the verbal content handlers 212 is adapted to take actionin response to one or more predefined verbal inputs, each verbal contentidentifier may be adapted to evaluate the association of the verbalinput with its respective verbal content handler 212. The predefinedverbal inputs may be identified and/or characterized by one or morepredefined features, for example, content, intent (intention, purpose,objective, goal, etc.), entity (object, item, element, target device,target application, etc.), dialog flows and/or the like. Each verbalcontent identifier may therefore be adapted to identify, in the verbalinput, features and evaluate their match with respective predefinedfeatures associated with its respective verbal content handler 212. Assuch each of the verbal content identifiers may be specifically andefficiently adapted, for example, designed, configured, trained and/orthe like according to the specific characteristics of its respectiveverbal content handler 212. In particular, each of the verbal contentidentifiers may be specifically adapted, for example, designed,configured, trained and/or the like to focus on identifying thepredefined features of its respective verbal content handler 212 in theverbal input. For example, one or more of the verbal content identifiersmay employ one or more classifiers (classification functions) trainedusing training sample dataset(s) to identify certain predefined featuresassociated with its respective verbal content handler 212.

Each verbal content identifier may extract one or more features from theverbal input using one or more verbal analysis tools, for example, atextual analysis, Natural Language Processing (NLP), speech recognition,Speech to Text (STT) conversion, speech synthesis, conversationconstruction and/or the like. The verbal content identifier may furtheremploy such verbal analysis tools which are known in the art, forexample, Watson Conversation Service (WCS), Watson Natural LanguageUnderstanding (NLU), regexp, API.AI, wit.ai and/or the like.

As shown at 106, for each feature it extracts from the verbal input,each of the verbal content identifier may compute a match confidencevalue for the extracted feature. The computed match confidence value mayindicate the probability (match level) that the extracted featureactually matches its respective predefined feature defined (associated)for the respective verbal content handler 212. The match confidencevalue may typically be represented as a normalized value mapped to arange of [0, 1] where 0 is lowest probability and 1 is highestprobability for the match. However, other implementations of the matchconfidence value may be used and the presented embodiment should not beconstrued as limiting. By computing the match confidence value for eachof the extracted feature(s), the verbal content identifier may evaluatean association of the verbal input in general and the extracted featuresin particular with the verbal content handler 212 associated with theverbal content identifier.

For example, assuming a home appliance control verbal content handler212 adapted to control operation of one or more home appliances, forexample, a light, an air-conditioning system, a shutter and/or the like.Such home appliance control verbal content handler 212 may be associatedwith one or more predefined features, for example, light, air condition,shutter, turn-on, turn-off, open, close and/or the like. The predefinedfeatures may further include locations, for example, kitchen, livingroom, bed room and/or the like. The verbal content identifier(s)associated with the home appliance control verbal content handler 212may therefore be adapted to extract one or more features from the verbalinput and evaluate a match between the extracted feature(s) and thepredefined features associated with the home appliance control verbalcontent handler 212 by computing the match confidence value for eachextracted feature with respect to a respective one of the predefinedfeatures.

In another example, assuming a navigation control verbal content handler212 adapted to control a navigation system for routing a vehicle, forexample, a car, according to instructions received from the user 250.Such navigation verbal content handler 212 may be associated with one ormore predefined features, for example, drive, travel, street, address,route and/or the like. The predefined features may further includegeographical locations, for example, city name(s), street name(s),landmark name(s) and/or the like. The verbal content identifier(s)associated with the navigation verbal content handler 212 may thereforebe adapted to extract one or more features from the verbal input andevaluate a match between the extracted feature(s) and the predefinedfeatures associated with the navigation verbal content handler 212 bycomputing the match confidence value for each extracted feature withrespect to a respective one of the predefined features.

Optionally, one or more of the verbal content identifiers extract one ormore context attributes relating to the verbal input, the user 250and/or other the like. The verbal content identifier(s) may furthercompute the match confidence value for each extracted context attributewhich may indicate a match probability to one or more predefined contextattributes associated with the respective verbal content handler 212.

The context attributes may include, for example, an emotion and/or asentiment of the user 250, for example, stress, anger, anxiety, joy,relaxation and/or the like. This may naturally be of major significancefor verbal inputs in the form of speech input spoken (uttered) by theuser 250. The verbal content identifier(s) may apply one or moreanalysis tools, for example, a voice analysis tool and/or the like toextract, i.e. evaluate and/or estimate one or more emotions and/orsentiments the user 250 may experience while uttering the verbal input.The verbal content identifier(s) may compute the match confidence valuefor each extracted emotion and/or sentiment which may indicate a matchprobability of the extracted emotion and/or sentiment with one or moreemotions and/or sentiments that may be predefined for the respectiveverbal content handler 212 associated with the verbal contentidentifier. For example, an emergency oriented verbal content handler212 adapted to initiate an emergency call to an emergency center may beassociated with one or more predefined emotions and/or sentiments, forexample, stress, anxiety and/or the like. The verbal contentidentifier(s) associated with the emergency oriented verbal contenthandler 212 may be adapted to extract the emotion(s) and/or sentiment(s)of the user 250 from the verbal input and compute the match confidencevalue for each extracted emotion and/or sentiment to evaluate the matchof the extracted emotion and/or sentiment to a respective one of thepredefined emotions and/or sentiments associated with the emergencyoriented verbal content handler 212.

The context attributes may also include, for example, a geographicallocation of the user 250, for example, a specific geographical location,indoor, outdoor and/or the like. The verbal content identifier(s) mayobtain such geographical location information from one or more locationdetection tools available in the computing device 201, for example, anInternet Protocol (IP) address, a navigation application (e.g. a GlobalPositioning System (GPS) based application), a triangulation system, afacility presence application (e.g. an application communicating withtransmitters deployed in a facility, for example, a home, an office, afactory and/or the like to identify the current location), an calendarapplication which may indicate a location of an event currently takingplace and/or the like. The verbal content identifier(s) may furtherobtain the geographical location from one or more sensors available inthe computing device 201, for example, a light sensor which may indicateindoor and/or outdoor illumination, a GPS sensor and/or the like. Theverbal content identifier(s) may compute the match confidence value forthe geographical location which may indicate a match probability to oneor more geographical locations that may be predefined for the respectiveverbal content handler 212 associated with the verbal contentidentifier. For example, the navigation verbal content handler 212adapted to control the navigation system for routing the vehicleaccording to instructions received from the user 250 may be associatedwith outdoor geographical locations, in particular, a road, a highwayand/or the like which may indicate (suggest) that the user 250 iscurrently driving the vehicle. The verbal content identifier(s)associated with the navigation verbal content handler 212 may thereforebe adapted to compute a high match confidence value for outdoorgeographical locations, in particular, the road, the highway and/or thelike while computing a low match confidence value for indoorgeographical locations.

In addition the context attributes may include one or more previousfeatures, for example, intent, entity, a dialog flow and/or the likeextracted from one or more previous verbal inputs. Moreover, one or moreof the verbal content identifiers may compute the match confidence valuefor one or more of the previous features.

Optionally, one or more of the verbal content identifier evaluatespresence of mandatory entity features in the verbal input which may bepredefined for the respective verbal content handler 212 the verbalcontent identifier is associated with. The mandatory entities may bepredefined to appear in verbal inputs that contain certain one or moreintent features associated with the respective verbal content handler212. The verbal content identifier(s) associated with such controlverbal content hander(s) 212 may therefore be adapted identify themandatory entity(s) in the verbal input and compute the match confidencevalue accordingly for the mandatory entity(s) associated with thecontrol verbal content handler 212. For example, assuming the homeappliance control verbal content handler 212 adapted to controloperation of one or more home appliances, for example, the light, theair-conditioning system, the shutter and/or the like. One or moremandatory entities may be predefined for such home appliance controlverbal content handler 212, for example, light, air condition, shutterand/or the like, in particular, the mandatory entity(s) may bepredefined with respect to one or more intent features, for example,turn-on, open and/or the like. The verbal content identifier(s)associated with the home appliance control verbal content handler 212may therefore be adapted identify the mandatory entity(s) in the verbalinput and compute the match confidence value for the mandatoryentity(s). For example, in case one of the associated verbal contentidentifier(s) identifies the turn-on intent feature with a certain matchconfidence value, the associated verbal content identifier(s) mayfurther search for one or more predefined mandatory entities, forexample, light, air-condition and/or the like and compute their matchconfidence value accordingly.

Optionally, one or more of the verbal content identifiers report and/orprovide one or more operational attributes relating to their respectiveverbal content handler 212.

The operational attributes may include, for example, a capability(state) of their respective verbal content handler 212 to manage(handle) the verbal input. For example, assuming a certain verbalcontent identifier evaluates a certain feature extracted from the verbalinput to match a certain predefined feature associated with therespective verbal content handler 212. However, assuming the certainpredefined feature is typically not used during an initial interactionof the user 250 with the respective verbal content handler 212 butrather later on in the dialog flow. Further assuming the respectiveverbal content handler 212 was not yet initiated, i.e. previous verbalinput(s) of the current session were not routed to the respective verbalcontent handler 212. In such case, the certain verbal content identifiermay indicate that the respective verbal content handler 212 is incapableof managing the (current) verbal input since in is not in a state (notinitialized) to manage the (current) verbal input. For example, assumingthe certain verbal content identifier extracted a “yes” entity featurewith a high match confidence value. However, the respective verbalcontent handler 212 which may typically be capable of managing the “yes”entity feature along one or more dialog flows is currently inun-initialized state in which the respective verbal content handler 212is incapable of managing the “yes” entity feature.

The operational attributes may also include a description of the verbalanalysis tool(s) used by one or more of the verbal content identifiersto extract the feature(s) from the verbal input. For example, atype/version of the NLP, a type/version of the speech recognition tooland/or the like.

The operational attributes may further include previous routinginformation relating to the respective verbal content handler 212. Forexample, a certain verbal content identifier may report a predefinednumber of previous routing events in which the respective verbal contenthandler 212 was selected to manage previous verbal input(s). Theoperational attributes may further include previous dialog flow(s)information for the respective verbal content handler 212. Each previousdialog flow may include and/or indicate one or more verbal inputsreceived by the respective verbal content handler 212 during the currentsession with the user 250.

One or more of the verbal content identifiers associated with respectiveverbal content handlers 212 may further report and/or provide one ormore operational attributes relating to one or more other verbal contenthandlers 212. For example, a certain verbal content identifierassociated with a certain verbal content handler 212 may obtain previousrouting information relating to one or more other verbal contenthandlers 212. The certain verbal content identifier may obtain suchrouting information, for example, from one or more verbal contentidentifiers associated with the other verbal content handler(s) 212.

In some embodiments of the present invention, the verbal content router210 may provide contextual information and/or operational information toone or more of the verbal content identifiers, in particular, thecontextual information and/or operational information may be providedtogether with the respective verbal input. The verbal content router 210may provide, for example, one or more context attributes and/or one ormore operational attributes to one or more to the verbal contentidentifier(s). The context attributes and/or one or more operationalattributes provided by the verbal content router 210 to a certain verbalcontent identifier may include attributes relating to the verbal contenthandler 212 associated with the certain verbal content identifier and/orto one or more other verbal content handlers 212. Moreover, one or moreof the verbal content identifiers may compute the match confidence valuefor one or more context attributes and/or operational attributesreceived from the verbal content router 210. For example, the verbalcontent router 210 may provide the certain verbal content identifierwith previous routing information and/or previous dialog flow(s)relating to the verbal content handler 212 associated with the certainverbal content identifier and/or to other verbal content handler(s) 212.Since the contextual information and/or operational information may bestored, managed and/or provided by the verbal content router 210, atleast some of the verbal content handlers 212 may be independent fromeach other and/or possibly oblivious to each other while still able totake advantage of the contextual information and/or operationalinformation relating to other verbal content handlers 212.

The verbal content identifiers may create a record, for example, a file,a structure, a list and/or the like comprising the computation resultsfor the extracted features. The verbal content identifiers mayoptionally provide the computation results for the context attributes,for the mandatory entities. The verbal content identifiers may furtherprovide the operational attributes. An exemplary such record ispresented in pseudocode excerpt 1 below.

Pseudocode Excerpt 1:

{ “Intents”: { “Intent” { “intent-name”:”turn-on” “confidence”:0.9 } }“Entities”: { “entity”:{ “entity-name”:”restaurant”,“value”:”McDonalds”, “confidence”:0.7 } } “context” : {“current-location”:”Haifa” } “emotions”:{ “emotion”:{“emotion-name”:”happy” “confidence”:0.6 } } “confidence-threshold”:0.85,“Capable”:true }

As shown at 108, the verbal content router 210 collects the computationresults provided by the plurality of verbal content identifiers for eachfeature extracted from the verbal input and selects a preferred one ofthe plurality of verbal content handlers 212 based on the computed matchconfidence values. For example, the verbal content router 210 mayidentify that a certain verbal content identifier computed a highestmatch confidence value for certain one or more extracted features andtherefore select the verbal content handler 212 associated with thecertain verbal content identifier to be the preferred verbal contenthandler 212. In another example, the verbal content router 210 mayaggregate match confidence values computed for multiple featuresextracted by one or more of the verbal content identifiers and computean aggregated match confidence value for the respective verbal contentidentifiers. The verbal content router 210 may select the preferredverbal content handler 212 to be the verbal content handler 212associated with the verbal content identifier which presents the highestaggregated match confidence value.

Optionally, the verbal content router 210 collects the computationresults provided by one or more of the verbal content identifiers forone or more of the context attributes and selects the preferred verbalcontent handler 212 using the match confidence value computed for thecontext attribute(s). In particular, the verbal content router 210 mayaggregate the match confidence value of the context attribute(s) withthe match confidence value computed by the same verbal contentidentifiers for the extracted features. This may significantly increasethe accuracy of the selection of the preferred verbal content handler212 since the context of the verbal input, the context of the user 250and/or the like may be indicative of the actual intention(s) of the user250.

For example, assuming a first verbal content identifier associated witha first verbal content handler 212 and a second verbal contentidentifier associated with a second verbal content handler 212 presentsimilar match confidence values for one or more extracted features. Thescope of the term similar may be predefined as a certain delta valuesuch that in case the match confidence values computed by the multipleverbal content identifiers are within the predefined delta value, thematch confidence values are considered similar. Further assuming thefirst verbal content handler 212 is associated with one or more certainemotions and/or sentiments, for example, stress and/or anxiety while thesecond verbal content handler 212 is associated with one or more otheremotions and/or sentiments, for example, joy. In such case, the firstand second verbal content identifiers may compute a significantlydifferent match confidence value for the emotion and/or sentimentcontext attribute depending on the emotion and/or sentiment of the user250 extracted from the verbal input. For example, assuming the user 250currently experiences stress, the first verbal content identifier maycompute a significantly high match confidence value for the emotioncontext attribute while the second verbal content identifier may computea significantly low match confidence value for the emotion contextattribute. In such case the verbal content router 210 may select thefirst verbal content handler 212 as the preferred verbal content handler212.

In another example, assuming a first verbal content identifierassociated with a first verbal content handler 212 and a second verbalcontent identifier associated with a second verbal content handler 212present similar match confidence values for one or more extractedfeatures. Further assuming the first verbal content handler 212 isassociated with indoor context while the second verbal content handler212 is associated with outdoor context. In such case, the first andsecond verbal content identifiers may compute a significantly differentmatch confidence value for the geographical location context attributedepending on the geographical location of the user 250. For example,assuming the user 250 is currently outdoors, the first verbal contentidentifier may compute a significantly low match confidence value forthe emotion context attribute while the second verbal content identifiermay compute a significantly high match confidence value for the emotioncontext attribute. In such case the verbal content router 210 may selectthe second verbal content handler 212 as the preferred verbal contenthandler 212.

In another example, the verbal content router 210 may select thepreferred verbal content handler 212 based on one or more of theprevious features provided by one or more of the verbal contentidentifiers. For example, assuming a certain verbal content handler 212was selected as the preferred verbal content handler 212 for one or moreprevious verbal inputs and/or part thereof, the verbal content router210 may evaluate the previous routing optionally coupled with thecomputed match confidence value assigned to the previous feature(s) inorder to select the preferred verbal content handler 212 to which the(current) verbal input is routed.

Optionally, the verbal content router 210 collects the operationalattribute(s) provided by one or more of the verbal content identifiersand uses the operational attribute(s) to select the preferred verbalcontent handler 212. This may further increase the accuracy of theselection of the preferred verbal content handler 212 since theoperational state of the verbal content hander(s) 212 may be indicativeof previous interaction with the user 250. In some embodiments of thepresent invention, the verbal content router 210 globally stores one ormore of the operational attributes. For example, the verbal contentrouter 210 may store the previous (historical) routing informationand/or dialog flow(s) reflecting routing of previous verbal inputs tothe verbal content handler(s) 212, in particular during the currentdialog session. Similarly to using the operational attribute(s) providedby the verbal content identifier(s), the verbal content router 210 mayuse the stored operational information for selecting the preferredverbal content handler 212 to which the (current) verbal input isrouted. Storing, managing and/or providing the operational attributes tothe verbal content identifiers may allow at least some of the verbalcontent handlers 212 to be independent from each other while still ableto take advantage of the contextual information and/or operationalinformation relating to other verbal content handlers 212.

For example, the verbal content router 210 may filter out, i.e. notselect one or more verbal content handlers 212 which are associated withverbal content identifier(s) which produced match confidence value(s)which fail to exceed a certain threshold. The verbal content handler 212may use a globally predefined threshold as the criterion for filteringout one or more of the verbal content handler 212. However, the verbalcontent handler 212 may use one or more predefined threshold provided byone or more of the verbal content identifiers as part of the operationalattributes. In such case the value of the predefined threshold may bedifferent for different verbal content identifiers. Optionally, thevalue of one or more predefined thresholds may be adapted for differentextracted features, for different context attributes and/or fordifferent mandatory entities.

In another example, the verbal content router 210 may filter out (notselect) one or more verbal content handlers 212 indicated by theirassociated verbal content identifier(s) as incapable, i.e. not in state,to manage the verbal input.

In another example, the verbal content router 210 may filter out (notselect) one or more verbal content handlers 212 for which theirassociated verbal content identifier(s) indicated that one or moremandatory entities are absent in the verbal input and/or the mandatoryentity(s) are assigned a match confidence value failing to exceed thepredefined threshold.

In another example, the verbal content router 210 may select thepreferred verbal content handler 212 using the previous routinginformation and/or the previous dialog flow information provided by oneor more of the verbal content identifiers. For example, assumingmultiple verbal content identifiers present significantly similar matchconfidence value for the feature(s) extracted from the verbal input. Insuch case, the verbal content router 210 may select the preferred verbalcontent handler 212 to be the verbal content handler 212 which mostrecently managed a previous verbal input. For example, assuming a firstverbal content identifier associated with a first verbal content handler212 and a second verbal content identifier associated with a secondverbal content handler 212 present similar match confidence values.Further assuming, a previous verbal input was routed to the secondverbal content handler 212 more recently than a routing was made to thefirst verbal content handler 212. In such case, the verbal contentrouter 210 may select the second verbal content handler 212 as thepreferred verbal content handler 212 since, based on temporal locality,the probability that the current verbal input is directed to the secondverbal content handler 212 may be higher than the probability that thecurrent verbal input is directed to the first verbal content handler212.

In another example, the verbal content router 210 may select thepreferred verbal content handler 212 using the description of the verbalanalysis tool(s) used by one or more of the verbal content identifiersto extract the feature(s) from the verbal input. For example, assumingmultiple verbal content identifiers present significantly similar matchconfidence value for the feature(s) extracted from the verbal input. Insuch case, the verbal content router 210 may evaluate which verbalanalysis tool(s) were applied by one or more of the verbal contentidentifiers and select, for example, the preferred verbal contenthandler 212 to be the verbal content handler 212 associated with theverbal content identifier that applied the most advanced verbal analysistool(s).

Optionally, the verbal content router 210 uses a predefined and/ordynamically set prioritization mechanism which prioritizes the verbalcontent handlers 212. The priority may apply to individual verbalcontent handlers 212 and/or to groups of verbal content handlers 212.For example, assuming a first group of verbal content handlers 212 isassigned a high priority compared to a second group of verbal contenthandlers 212. The verbal content router 210 may give precedence to thefirst group when selecting the preferred verbal content handler 212. Insuch case the verbal content router 210 may select the preferred verbalcontent handler 212 from the first group even if a verbal contentidentifier associated with a verbal content handler 212 of the secondgroup presents a higher match confidence match. However, in case none ofthe verbal content identifier associated with a verbal content handler212 of the first group presents a match confidence match exceeding thethreshold, the verbal content router 210 may select the preferred verbalcontent handler 212 from the second group.

Optionally, the verbal content router 210 selects a default verbalcontent handler 212 as the preferred verbal content handler 212. Forexample, the verbal content router 210 selects the default verbalcontent handler 212 in case none of the verbal content identifiersproduces a match confidence value that exceeds the predefined threshold.This may apply to match confidence value computed for the extractedfeature(s), for the context attribute(s) and/or for the mandatoryentity(s). The verbal content router 210 may select, for example,browser type verbal content handler 212 as the preferred verbal contenthandler 212 and instruct the browser to access a certain website inwhich the user 250 may browse to accomplish his intention(s). The verbalcontent router 210 may optionally select the default verbal contenthandler 212 according to one or more of the context attributes. Forexample, assuming the verbal content handler 212 identifies, based onthe context attributes provided by one or more of the verbal contentidentifiers, that the user 250 is located in a certain geographicallocation, for example, a movie theatre complex. In such case, the verbalcontent handler 212 may select, for example, parking finding verbalcontent handler 212 as the preferred verbal content handler 212 allowingthe user 250 to seek guidance for locating a parking lot and/or aparking place.

As shown at 110, the verbal content router 210 routs the verbal input tothe selected verbal content handler 212. Naturally, in case the verbalinput was segmented, each of the segments is processed through the steps104-110 of the process 100 and is routed to one the verbal contenthandlers 212 selected for the respective segment.

The selected verbal content handler 212 may initiate one or moreactions, for example call a function, apply a feature, initiate anoperation and/or the like as indicated by the user 250 through theverbal input. Naturally the actions initiated by the selected verbalcontent handler 212 are supported by the computing device 201. Forexample, assuming the computing device 201 is a Smartphone, the actionsinitiated by the selected verbal content handler 212 may include, forexample, initiating a mobile application such as for example, initiate aphone call to a selected contact, send a text message and/or an emailmessage to a selected contact, browse to a specific website, take apicture, start recording an audio input, play a media content, turnON/OFF the computing device, set volume level and/or the like. Inanother example, assuming the computing device 201 is a control systemof an autonomous vehicle, the actions initiated by the selected verbalcontent handler 212 may include, for example, starting/shutting thevehicle, setting a driving profile, turning lights ON/OFF, navigating toa certain geographical location, play a media content at an infotainmentsystem of the vehicle and/or the like. In another example, assuming thecomputing device 201 is a control system for one or more homeappliances, for example, a virtual personal assistant, the actionsinitiated by the selected verbal content handler 212 may include, forexample, turning ON/OFF a home appliance, initiating an application at ahome appliance, checking for status of a home appliance and/or the like.

It is expected that during the life of a patent maturing from thisapplication many relevant systems, methods and computer programs will bedeveloped and the scope of the term verbal analysis tool is intended toinclude all such new technologies a priori.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”. This termencompasses the terms “consisting of” and “consisting essentially of”.

The phrase “consisting essentially of” means that the composition ormethod may include additional ingredients and/or steps, but only if theadditional ingredients and/or steps do not materially alter the basicand novel characteristics of the claimed composition or method.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

The word “exemplary” is used herein to mean “serving as an example, aninstance or an illustration”. Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

What is claimed is:
 1. A computer implemented method of routing a verbalinput to one of a plurality of handlers, comprising: using at least oneprocessor adapted to execute a code, said code is adapted for: receivinga verbal input from a user; applying a plurality of verbal contentidentifiers to said verbal input, each of said plurality of verbalcontent identifiers is adapted to evaluate an association of said verbalinput with a respective one of a plurality of handlers by computing amatch confidence value for at least one feature extracted from saidverbal input, said at least one feature comprises at least one of: anintent expressed by said user and an entity indicated by said user;extracting from said verbal input at least one of an emotion of saiduser and a sentiment of said user; routing said verbal input to aselected one of said plurality of handlers based on said matchingconfidence value computed by said plurality of verbal contentidentifiers and according to a match between said extracted at least oneemotion and sentiment, and at least one of a predefined emotion and apredefined sentiment associated with each of said plurality of handlers,said selected handler is adapted to initiate at least one action inresponse to said verbal input; wherein each of said plurality of verbalcontent identifiers is associated with a respective one of saidplurality of handlers by adapting said each verbal content identifier toevaluate said association according to at least one predefined featuredefined for said respective handler; wherein said computed matchconfidence value used for said routing, is an aggregated matchconfidence value, calculated by aggregating a plurality of confidencevalues each computed for a different one of said at least one predefinedfeature.
 2. The computer implemented method of claim 1, wherein saidverbal input comprises at least one member of a group consisting of:textual verbal input and speech verbal input.
 3. The computerimplemented method of claim 1, further comprising segmenting said verbalinput to a plurality of segments and routing each of said plurality ofsegments to one of said plurality of handlers according to said matchconfidence value computed by said plurality of verbal contentidentifiers for said each segment.
 4. The computer implemented method ofclaim 1, wherein said match confidence value indicates a probability ofsaid at least one feature to match a respective predefined feature. 5.The computer implemented method of claim 1, wherein said intent isextracted from said verbal input using at least one verbal analysistool, said intent is a member of a group consisting of: an intention, apurpose, an objective and a goal.
 6. The computer implemented method ofclaim 1, wherein said entity is extracted from said verbal input usingat least one verbal analysis tool.
 7. The computer implemented method ofclaim 1, further comprising selecting said selected handler based on atleast one context attribute provided by at least one of said pluralityof verbal content identifiers.
 8. The computer implemented method ofclaim 7, wherein said at least one context attribute comprises at leastone of: a geographical location of said user obtained from at least onelocation detection tool, and at least one previous feature extractedfrom at least one previous verbal input.
 9. The computer implementedmethod of claim 7, further comprising selecting said selected handlerbased on detection of at least one mandatory entity extracted by atleast one of said plurality of verbal content identifiers, said at leastone mandatory entity is predefined to appear in said verbal input inconjunction with said intent.
 10. The computer implemented method ofclaim 1, further comprising selecting said selected handler based on atleast one operational attribute provided by at least one of saidplurality of verbal content identifiers.
 11. The computer implementedmethod of claim 10, wherein said at least one operational attributecomprises at least one of: a threshold value, a capability of saidrespective handler to manage said verbal input, a description of ananalysis applied by at least one of said plurality of verbal contentidentifiers to extract said at least one feature, a routing informationrelating to at least one previous verbal input, and information obtainedfrom at least another one of said plurality of handlers.
 12. Thecomputer implemented method of claim 1, further comprising selectingsaid selected handler according to a priority assigned to at least someof said plurality of verbal content identifiers.
 13. The computerimplemented method of claim 1, further comprising filtering out at leastone of said plurality of handlers associated with one of said pluralityof verbal content identifiers presenting said confidence value whichfails to exceed a predefined threshold.
 14. The computer implementedmethod of claim 1, further comprising filtering out at least one of saidplurality of handlers indicated by an associated one of said pluralityof verbal content identifiers as incapable of managing said verbalinput.
 15. The computer implemented method of claim 1, furthercomprising routing said verbal input to a recent handler of saidplurality of handlers in case a similar said confidence value iscomputed by multiple verbal content identifiers of said plurality ofverbal content identifiers, said recent handler is a most recent handlerto which a previous verbal input was routed among a group of handlersassociated with said multiple verbal content identifiers.
 16. Thecomputer implemented method of claim 1, further comprising routing saidverbal input to a default handler of said plurality of handlers in casesaid confidence value computed by said plurality of verbal contentidentifiers fails to exceed a predefined threshold.
 17. A system forrouting a verbal input to one of a plurality of handlers, comprising: atleast one processor adapted to execute code, the code comprising: codeinstructions to receive a verbal input from a user; code instructions toapply a plurality of verbal content identifiers to said verbal input,each of said plurality of verbal content identifiers is adapted toevaluate an association of said verbal input with a respective one of aplurality of handlers by computing a match confidence value for at leastone feature extracted from said verbal input, said at least one featurecomprises at least one of: an intent expressed by said user and anentity indicated by said user; and code instructions to extract fromsaid verbal input at least one of an emotion of said user and asentiment of said user; code instructions to route said verbal input toa selected one of said plurality of handlers based on said matchingconfidence value computed by said plurality of verbal contentidentifiers and according to a match between said extracted at least oneemotion and sentiment, and at least one of a predefined emotion and apredefined sentiment associated with each of said plurality of handlers,said selected handler is adapted to initiate at least one action inresponse to said verbal input; wherein each of said plurality of verbalcontent identifiers is associated with a respective one of saidplurality of handlers by adapting said each verbal content identifier toevaluate said association according to at least one predefined featuredefined for said respective handler; wherein said computed matchconfidence value used for said routing, is an aggregated matchconfidence value, calculated by aggregating a plurality of confidencevalues each computed for a different one of said at least one predefinedfeature.
 18. A computer program product for routing a verbal input toone of a plurality of handlers, comprising: a non-transitory computerreadable storage medium; first code instructions for receiving a verbalinput from a user; second code instructions for applying a plurality ofverbal content identifiers to said verbal input, each of said pluralityof verbal content identifiers is adapted to evaluate an association ofsaid verbal input with a respective one of a plurality of handlers bycomputing a match confidence value for at least one feature extractedfrom said verbal input, said at least one feature comprises at least oneof: an intent expressed by said user and an entity indicated by saiduser; and third code instructions for extracting from said verbal inputat least one of an emotion of said user and a sentiment of said user;fourth code instructions for routing said verbal input to a selected oneof said plurality of handlers based on said matching confidence valuecomputed by said plurality of verbal content identifiers and accordingto a match between said extracted at least one emotion and sentiment,and at least one of a predefined emotion and a predefined sentimentassociated with each of said plurality of handlers, said selectedhandler is adapted to initiate at least one action in response to saidverbal input; wherein said first, second, third and fourth programinstructions are executed by at least one processor from saidnon-transitory computer readable storage medium; wherein each of saidplurality of verbal content identifiers is associated with a respectiveone of said plurality of handlers by adapting said each verbal contentidentifier to evaluate said association according to at least onepredefined feature defined for said respective handler; wherein saidcomputed match confidence value used for said routing, is an aggregatedmatch confidence value, calculated by aggregating a plurality ofconfidence values each computed for a different one of said at least onepredefined feature.